Remaining useful life (RUL) of running machines, machine components and equipment and the like is important information to system planning and operation. Known RUL in system planning can lead to more efficient use of the machine, less down time, and less faults. This, in turn, can lead to cost savings, avoidance of sudden breakdowns while in operation, and appropriate selection of installation or maintenance time. Thus, industries have long sought a method of predicting or estimating the RUL of various types of machines.
Estimating the RUL of a machine, machine component, system of a machine or other equipment is known in the art as prognostics. Predicting remaining life is not straightforward because, ordinarily, remaining life is conditional upon a variety of factors including future usage conditions. Examples of equipment that may benefit from the use of remaining life estimates are military and commercial fielded vehicles including automobiles, tanks, helicopters and other aircraft (both military and commercial), medical equipment, industrial or agricultural equipment, and power plants.
A common approach to prognostics is to employ a data-driven approach to take advantage of time series data where equipment behavior has been tracked via sensor outputs during normal operation up until an end of equipment useful life. The end of equipment useful life may represent a totally non-functioning state of the equipment, for example, equipment failure. The end of equipment useful life can also represent a state of the equipment wherein the equipment no longer provides expected results. Alternatively, the end of useful life may be defined as when the equipment reaches a condition of imminent failure. When a reasonably-sized set of these observations exists, pattern recognition algorithms can be employed to recognize these trends and predict remaining life. These predictions are often made under the assumption of near-constant future conditions. However, such run-to-end-of-equipment-useful-life data are often not available because, when the observed system is complex, expensive, and, safety is important, faults will be repaired before they lead to the end of equipment useful life. This deprives the data driven approach from information necessary for its proper application.